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Multiple Object Tracking (MOT) aims to find bounding boxes and identities oftargeted objects in consecutive video frames. While fully-supervised MOTmethods have achieved high accuracy on existing datasets, they cannotgeneralize well on a newly obtained dataset or a new unseen domain. In thiswork, we first address the MOT problem from the cross-domain point of view,imitating the process of new data acquisition in practice. Then, a newcross-domain MOT adaptation from existing datasets is proposed without anypre-defined human knowledge in understanding and modeling objects. It can alsolearn and update itself from the target data feedback. The intensiveexperiments are designed on four challenging settings, including MOTSynth toMOT17, MOT17 to MOT20, MOT17 to VisDrone, and MOT17 to DanceTrack. We thenprove the adaptability of the proposed self-supervised learning strategy. Theexperiments also show superior performance on tracking metrics MOTA and IDF1,compared to fully supervised, unsupervised, and self-supervisedstate-of-the-art methods.

UTOPIA: Unconstrained Tracking Objects without Preliminary Examination via Cross-Domain Adaptation